A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

Lee, Tyler; Theunissen, Frédéric E.

doi:10.1098/rspa.2015.0309

Cited by 14 publications

(8 citation statements)

References 76 publications

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“…The first step is to convert the wav file into a spectrogram, a 2D tensor, which is "obtained using the short-time Fourier transform since it describes the evolution of the frequency components over time" [6]. The second step is to implement a noise suppression algorithm using Gaussian-shaped frequency filter for the spectrogram images using SciPy to limit their amplitude bandwidth [3]. To further remove any noise from the audio, the filter applies a standard deviation of 75 Hz and 150Hz for heart and lung sounds to ensure 99.7% of the frequencies are within the respective ranges.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

Prabu¹

2021

Preprint

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<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

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Section: Preprocessing Methodsmentioning

confidence: 99%

SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

Prabu¹

2021

Preprint

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“…Further studies [108]- [110] binarize the data lables rather than using real-valued data, as shown in Figure 10. Several studies have been conducted on wind noise reduction [109]- [117]. However, for a maritime scenario it is still an open problem.…”

Section: Maritime Object Localisation By Soundmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

111

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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“…But, the performance was not effective. Lee et al [15] defined a new Spectro-Temporal Detection-Reconstruction (STDR) scheme in which the speech was extracted from background noise via learning a spectro-temporal feature space continuously. Also, a static nonlinearity was applied for projecting the noisy speech.…”

Section: Literature Surveymentioning

confidence: 99%

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

Selvaraj

Chandra

2022

IFS

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The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

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A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

Cited by 14 publications

References 76 publications

SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

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